tfp.mcmc.TransitionKernel

Base class for all MCMC TransitionKernels.

This class defines the minimal requirements to efficiently implement a Markov chain Monte Carlo (MCMC) transition kernel. A transition kernel returns a new state given some old state. It also takes (and returns) "side information" which may be used for debugging or optimization purposes (i.e, to "recycle" previously computed results).

Attributes
`is_calibrated`	Returns `True` if Markov chain converges to specified distribution. `TransitionKernel`s which are "uncalibrated" are often calibrated by composing them with the `tfp.mcmc.MetropolisHastings` `TransitionKernel`.

Attributes

is_calibrated

Returns True if Markov chain converges to specified distribution.

TransitionKernels which are "uncalibrated" are often calibrated by composing them with the tfp.mcmc.MetropolisHastings TransitionKernel.

Methods

`bootstrap_results`

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bootstrap_results(
    init_state
)

Returns an object with the same type as returned by one_step(...)[1].

Args
`init_state`	`Tensor` or Python `list` of `Tensor`s representing the initial state(s) of the Markov chain(s).

Returns
`kernel_results`	A (possibly nested) `tuple`, `namedtuple` or `list` of `Tensor`s representing internal calculations made within this function.

`copy`

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copy(
    **override_parameter_kwargs
)

Non-destructively creates a deep copy of the kernel.

Args
`**override_parameter_kwargs`	Python String/value `dictionary` of initialization arguments to override with new values.

Returns
`new_kernel`	`TransitionKernel` object of same type as `self`, initialized with the union of self.parameters and override_parameter_kwargs, with any shared keys overridden by the value of override_parameter_kwargs, i.e., `dict(self.parameters, **override_parameters_kwargs)`.

`one_step`

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@abc.abstractmethod
one_step(
    current_state, previous_kernel_results, seed=None
)

Takes one step of the TransitionKernel.

Must be overridden by subclasses.

Args
`current_state`	`Tensor` or Python `list` of `Tensor`s representing the current state(s) of the Markov chain(s).
`previous_kernel_results`	A (possibly nested) `tuple`, `namedtuple` or `list` of `Tensor`s representing internal calculations made within the previous call to this function (or as returned by `bootstrap_results`).
`seed`	Optional, a seed for reproducible sampling.

Returns
`next_state`	`Tensor` or Python `list` of `Tensor`s representing the next state(s) of the Markov chain(s).
`kernel_results`	A (possibly nested) `tuple`, `namedtuple` or `list` of `Tensor`s representing internal calculations made within this function.